Biased recommender system based on age parameter

ABSTRACT

The present invention relates to an apparatus, a method and a computer program product for controlling a recommender system, wherein a bias is applied to the output of a recommender in order to favour newly added services and content sources. The bias may decay in time and/or with the number of ratings given to content coming from the new service.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is the U.S. National Stage of International ApplicationNo. PCT/EP2009/067153 filed on Dec. 15, 2009 which was published inEnglish on Jul. 1, 2010 under International Publication Number WO2010/072616.

DESCRIPTION

1. Field of the Invention

The present invention relates to an apparatus, a method, and a computerprogram product for controlling a recommender system for at least onecontent item.

2. Background of the Invention

Hard-disk drives and digital video compression technologies have createda possibility of time-shifting live television (TV) and recording alarge number of TV shows in high quality without having to worry aboutthe availability of tapes or other removable storage media. At the sametime, digitalization of audiovisual signals has multiplied the number ofcontent sources for an average user. Hundreds of channels are availableusing a simple parabolic antenna and a TV receiver. Huge amounts ofvideo clips are published daily on the Internet across various services,and all major content producers are already making their entire contentlibraries available online. As a consequence, thousands of potentiallyincreasing programs are made available every day and can be recorded andstored locally for later access.

However, in view of this enormous amount of offered content items,individual content selection becomes an important issue. Informationthat does not fit to a user profile should be filtered out and the rightcontent item that matches a user's needs and preferences (e.g. userprofile) should be selected.

Recommender systems address these problems by estimating a degree oflikeliness of a certain content item for a certain user profile andautomatically ranking the content item. This can be done by comparing acontent item's characteristics (e.g. features, metadata, etc.) with auser profile or with similar profiles of other users. Thus, recommendersystems can be seen as tools for filtering out unwanted content andbringing interesting content to the attention of the user.

The use of recommender technology is steadily being introduced into themarket. Among various examples, websites offer a recommender to supportusers in finding content items (e.g. movies) they like, and electronicsdevices (e.g. personal video recorders) use recommender for automaticfiltering content items. Recommender systems are increasingly beingapplied to individualize or personalize services and products bylearning a user profile, wherein machine learning techniques can be usedto infer the ratings of new content items.

Commonly used recommender techniques are collaborative filtering andnaïve Bayesian classification. Thereby, from a vast amount of contentitems, only those items that match a profile of a user or group of userscan be retrieved. Recommenders are typically offered as stand-aloneservices or units, or as add-ons (e.g. plug-ins) to existing services orunits. They increasingly appear in consumer devices, such as TV sets orvideo recorders. Recommenders typically require user feedback to learn auser's preferences. Implicit learning frees the user from having toexplicitly rate items, and may by derived by observing user actions suchas purchases, downloads, selections of items for play back or deletion,etc. Detected user actions can be interpreted by the recommender andtranslated into a rating. For example, a recommender may interpret apurchase action as positive rating, or, in case of video items, a totalviewing duration of more/less than 50% may imply a positive/negativerating.

When a user subscribes to a new service (e.g. a broadcast channel, avideo-on-demand service, an Internet video website, etc.), he/she wouldlike and expects to see content from the newly subscribed service.However, when access to the content is mediated by a recommender system(e.g. as in a personal TV concept), the probability that a content itemfrom a new content source is recommended to the user may be low becausethe newly added service is interpreted by the recommender system as onlyone other content service added to a pool of existing content services,and therefore there is no special reason why a content item from thenewly acquired content service should be preferred. Moreover, initially,the recommender's profile does not contain any ratings related to thenewly acquired content service. This may result in an inadequateselection of a newly acquired content service and thus to a sub-optimaloperation of the recommender system.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide an improvedrecommender operation, by means of which content items of newly acquiredcontent services are adequately rated and/or recommended.

This object is achieved by an apparatus as claimed in claim 1, a methodas claimed in claim 10, and a computer program product as claimed inclaim 13.

The apparatus comprises a recommender for calculating a predicted ratingvalue of a content item (e.g. based on metadata or a collaborativefiltering techniques etc.) received from a content source. According tothe invention, this rating value is corrected by means of an agedeterminator and a biasing unit. The age determinator is adapted toderive an age parameter from at least one of said metadata and a numberof rating values obtained from a user or predicted for received contentitems of said content. The biasing unit is adapted to generate acorrection value based on the age parameter and to apply the correctionvalue thus generated to the predicted rating value. Thus, the predictedrating value is corrected based on the age of subscription to thecontent source, which may be determined for example based on themetadata used for calculating the rating value.

Metadata shall mean any information added or associated to a particularcontent item to be rated, i.e. “data about data”, of any sort in anymedia. An item of metadata may describe an individual content item, or acollection of data including multiple content items and hierarchicallevels, for example a database scheme. Additionally, the metadata may bedefinitional data that provides information about or documentation ofother a content item. Or group of content items. For example, metadatamay document data about data elements or attributes (name, size, datatype, etc) of a content item or group of content items, and data aboutrecords or data structures (length, fields, columns, etc) of a contentitem or group of content items, and data about data (where it islocated, how it is associated, ownership, etc.) of a content item orgroup of content items. Metadata may also include descriptiveinformation about the context, quality and condition, or characteristicsof the content item or group of content items. Accordingly, the outputresult or rating of a recommender can be biased in order to favour newlyadded services and content sources. In this way, content items receivedfrom newly acquired content services are preferably recommended and thusmore preferably selected, so that content items received from such newservices or sources are recommended more often.

According to a first aspect, the age determinator may comprise adeterminator for determining a subscription time (e.g. based on themetadata), and a calculator for calculating the age parameter based onthe subscription time and an actual time. Thereby, a straight forwardway for assessing oldness of the source of a content item can beprovided. The subscription time provided by the metadata of the contentitem is compared to the actual time or time instant, so that a directmeasure of the oldness or degree of novelty can be obtained.

According to a second aspect which can be combined with the above firstaspect, the age determinator may be adapted to determine thesubscription time by accessing a look-up table. The look-up table maystore a relation between a source identification or identifier and thesubscription time, so that the subscription time can be provided in aneasy and fast manner. The look-up table may be stored in a programmablememory, so that it can be updated whenever a subscription to a newcontent source has been initiated. As an alternative, the subscriptiontime may be stored in a service profile of the concerned user.

According to a third aspect which can be combined with any one of theabove first and second aspects, the biasing unit may be adapted todecrease the correction value with increasing age parameter or withincreasing number of rating values given by a user or predicted by therecommender for received content items of the content source. Thereby,the bias applied to the output of the recommender decays in time and/orwith the number of ratings given to content items received from a newcontent source.

In a specific example, the biasing unit may be adapted to decrease thecorrection value in a linear or exponential relation to time or thenumber of rating values.

According to a fourth aspect which can be combined with any one of theabove first to third aspects, the recommender may be adapted tocalculate said predicted rating value in a range [0, 1]. Thus, theoutput or predicted rating of the recommender can be provided in theform of a probability between the values “0” and “1”, which can be usedto indicate the user preference as a like-degree where “0” indicates“dislike” and “1” indicates “like”. Of course, any other kind or type ofvalue could be used as well (e.g. 5-stars scale).

According to a fifth aspect which can be combined with any one of theabove first to fourth aspects, the age parameter may be a novelty valuewhich indicates how long the content source has been available in therecommender system. In a specific example, the novelty value may be apositive real number less than or equal to 1. The novelty value can beset to “1” when a content source is added to the recommender system forthe first time, so that the biased output value of the recommender willbe controlled to its maximum value.

According to a sixth aspect which can be combined with any of the abovefirst to fifth aspects, the age parameter may represent the age ofsubscription to the content source, and the correction value may resultin a higher rating value for younger subscriptions.

According to a seventh aspect which can be combined with any one of theabove first to eighth aspects, an update of a content pre-selectionfilter may be triggered in response to an addition of a new contentsource. Thereby, it can be assured that content items received from thenew source will pass the pre-selection filter.

According to an eighth aspect which can be combined with any one of theabove first to seventh aspects, a user input operation for allocating apersonal channel to the new content source may be triggered in responseto the addition of a new content source. Here, “personal channel” isintended to mean that the channel has associated a new pre-selectionfilter. Thus, subscription to a new service or a new content sourcetriggers a dialogue with the user to select or create a new personalchannel (i.e. with associated pre-selection filter) for the new contentsource. In case of a recommender system for a personal TV set, a new TVstation received by a satellite service (e.g. a free-to-air satellite TVservice) is automatically recommended by the recommender if it has beenselected as a new content source, e.g., in a user profile (such as MyPersonal TV guide).

It is noted that the above control or recommender apparatus can beimplemented as discrete hardware circuitry with discrete hardwarecomponents, as an integrated chip, as an arrangement of chip modules, oras a signal processing device or computer device or chip controlled by asoftware routine or program stored in a memory.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will now be described, by way of example, based onembodiments with reference to the accompanying drawings, wherein:

FIG. 1 shows a schematic block diagram of a recommender system accordingto an embodiment of the present invention; and

FIG. 2 shows a schematic block diagram of a biased recommender accordingto an embodiment of the present invention.

DESCRIPTION OF EMBODIMENTS

Embodiments of the present invention will now be described based on anexemplary recommender system which generates ratings on content items,such as books, TV programs, movies, etc.

Fig. 1 shows a schematic block diagram of a recommender system whichcomprises an information data store 103 connected to a source (S) 101.The source 101 may, for example, be an electronic program guide (EPG)service on the Internet, which provides information data on TV programs.The information data store 103 can be connected to at least onepre-selection filter (F) 105 which is associated with a personalizedcontent channel and filters content items accordingly. It is noted thatany number of personalized content channels could be provided. Theoutput of the pre-selection filter 105 is connected to a respectivebiased recommender engine (B-RE) 107. Thus, each personalized contentchannel may have an own biased recommender engine 107 associatedtherewith. Each biased recommender engine 107 and hence personalizedcontent channel has a profile (P) 109 associated therewith. The outputof the biased recommender engine 107 is connected to a scheduler (SCH)111. The scheduler 111 is connected to a storage device 113 (e.g. a setof hard-disk drives), and to a selector (SEL) 115. The information datastore 103 is also connected to a content source (CS) 117. The contentsource 117 provides for example at least audio/video information in abroadcasting or on-demand fashion. In addition, the content source 117may provide information data, e.g., EPG information inside the verticalblanking interval of the video signal, or MPEG-7metadata on segments ofa particular content item (e.g. the scene boundaries of a movie). Thecontent source 117 is connected to the selector 115 comprising at leastone set of content isolation means (e.g. a tuner or the like) whichallows to isolate one or more content items for recording on the storagedevice 113. The output of the selector 115 is connected to the storagedevice 113.

The operation of the apparatus of FIG. 1 will now be described.Information data of a current content item to be played out on apersonalized content channel is gathered from the source (Internet) 101or obtained via other means, e.g., via transmission in the veritcalblanking interval of an analog TV broadcast signal or via digital videobroadcast (DVB) transport streams, or combinations of any of the above.The content item may be a TV program, data stream containing Videoand/or audio data or a segment of a program etc.

The information data may comprise a plurality of attributes andattribute values associated with the content item such as title, actors,director and genre. Each profile 109 is based on the information datatogether with data indicating the “like” or “dislike” of the user. Therating of a “like” and “dislike” can be based on feedback or contentitems that pass the associated pre-selection filter 105. This feedbackcan be given as explicit rating by the users that use the particularpersonalized content channel. The ratings can be made in several ways.For example, the user can, using a remote control device, indicate for acurrently selected content item or a given attribute of the currentcontent item his rating (“like” or “dislike”) by pressing appropriatebuttons on a user interface (e.g. the remote control device) whilstbearing the current content item. Alternatively, the behaviour of theuser can be observed. For example, if the user watches a current contentitem for more than a predefined time interval (for example, 20 minutes),this could automatically indicate “like”. In a more advanced setting, a“like” degree on a discrete or continuous scale can be provided orcalculated instead of just a binary “like” or “dislike” classification.

When information data of a content item passes the filter 105, thisinformation data is forwarded to the biased recommender engine 107. Thebiased recommender engine calculates a biased “like” degree or rating,based on its associated profile 109, for this subsequent content item.The information data associated to the subsequent content item is thenforwarded, along with the computed rating, to the scheduler 111, whichsubsequently computes recording schedule that will be used to schedulethe recording of content items offered by the recommender engine 107onto the storage device 113. In particular, the scheduler 111 mayprimarily consider the content items of high like degree or rating whilestill considering sufficient new content for each personalized contentchannel. To this end, the recording schedule computed by the scheduler111 is used to instruct the scheduler 115 to select the content itemsavailable from the content source 117 to record them on the storagedevice 113.

Use or user profiles can be derived using three basic methods: implicitprofiling; explicit profiling; and feedback profiling. Implicitprofiling methods derive content use profiles unobtrusively from theuser's use histories, e.g., sets of TV shows watched and not watched.Explicit profiling methods derive content use profiles from user'sanswered questions as that include explicit questions about what theuser likes and dislikes. Feedback profiling methods derive use profilesfrom content items for which a user has provided ratings of the degreeof like or dislike.

According to the embodiment, the biased recommender engine 107 comprisesa biasing unit, component or function which is adapted or configured toprovide a biased output so as to favour newly added services and contentsources. The bias can be applied by a correction value which is combinedwith the recommender output. This correction value may decay in timeand/or with the number of ratings given to content items by the user orby the recommender received from the new content service. Thereby, theprobability that content from a new source is recommended to the usercan be increased, since the biased output can ensure that a content itemfrom a newly acquired service (i.e. content source) is preferred even ifthe recommender's profile 109 does not contain any ratings related tothe newly acquired service.

FIG. 2 shows a schematic block diagram of an implementation example ofthe biased recommender engine 107 of FIG. 1. The components or blocksshown in FIG. 2 may be implemented as hardware circuits or componentsor, alternatively, as software routines stored in a memory of a computeror processor device.

When a new content item is available, its features or metadata MD_(i)are supplied to a recommender (RE) 1072 which calculates or determines apredicted rating, for example in the form of a posterior probabilityP_(r) in the range [0, 1] which thus indicates a like-degree of thegiven content item for a user (e.g. where “0” indicates “dislike” and“1” indicates “like”). This probability P_(r) is supplied to a biasingunit (B) 1074 which applies a bias (e.g. correction value) to theprobability P_(r) depending on an age degree or novelty degree of thecontent source from which the concerned content item has been received.It is noted that the term “content source” can be interpreted as anyservice or source from which a content item is received, e.g., abroadcast channel or video-on-demand service or the like.

Thus, each content source (e.g. the content source 117 of FIG. 1) can becharacterized by a novelty value or other kind of age parameter thatindicates how long the content source has been available in the currentrecommender system.

In the present implementation example, the novelty value of a contentsource may be selected as a real number between “0” and “1”. The noveltyvalue can be set to “1” when the content source is added to the systemfor the first time, and therefore is to be regarded as a new contentsource. Additionally, a processing may be provided to decrease thenovelty value (e.g. in a linear or exponential manner) with time and/orwith the amount of ratings given to content items stemming from aconcerned content source. Thereby, novelty of a content source and thusthe bias or correction value decays with time. A biased or correctedprobability P_(rb) is thus obtained from the biasing unit 1074.

As can be gathered from the implementation example of FIG. 2, thebiasing unit 1074 may have two control inputs for controlling thecorrection value or bias. On one hand, a novelty value ν may be suppliedfrom a calculator 1078 and, on the other hand, an additional oralternative control output of the recommender 1072 may be used toindicate an amount of ratings received from the user or generated by therecommender 1072 for the concerned content item. This control value maybe derived from a look-up table (not shown) managed by the recommendersystem or from a service profile of the user.

The metadata MD_(i) of the concerned content item is also applied to adeterminator (DET) 1076 which derives a source identification oridentity S_(i) from the metadata MD_(i) and determines a subscriptiontime T_(s) at which the user has subscribed to the content service fromwhich the concerned content item has been received. The subscriptiontime T_(s) may be directly supplied by the metadata MD_(i) or may bederived from a look-up table (LUT) 1077 which can be updated by therecommender system whenever a user subscribes to a new content source.

The subscription time T_(s) is then supplied to the calculator (NV) 1078which calculates the novelty value ν based on the subscription timeT_(s). This can be achieved by comparing the subscription time T_(s) toan actual time t obtained from a timer 1079 which may be provided at thebiased recommender engine 107 or somewhere else in or at the recommendersystem.

For example, given a content item i, with source identity S_(i), itsnovelty value at an instant t≧0 after the moment of subscription (T₀,t=0) is given by:

$\begin{matrix}{{\upsilon(t)} = \left\{ \begin{matrix}0 & {{{if}\mspace{14mu} t} \geq {T_{0}\mspace{14mu}{and}\mspace{14mu} t} < T_{S}} \\{1 - \frac{t - T_{S}}{T_{0} - T_{S}}} & {{{if}\mspace{14mu} T_{S}} \leq t < T_{0}}\end{matrix} \right.} & (1)\end{matrix}$

Where T₀>T_(s) is the time after subscription to a new content source,at which a new content source cannot be considered novel anymore.

Assuming that the above posterior probability for the present contentitem i is given by P_(r)(+|i), the biased posterior probabilityP_(rb)(+|i) is given by:Pr _(b)(+|i)=ν(S _(i) ,t)·1+(1−ν(S _(i) ,t))·Pr(+|i)  (2)

To thereby provide a gradual change from a maximal probability P_(rb)=1to the actual posterior probability P_(r) as time progresses.

In recommender systems where content access is mediated not only by arecommender engine, but also by a pre-selection filter that pre-selectscontent items (e.g. by means of logic rules) before they are evaluatedby the recommender engine (such as the pre-selection filter 105 of FIG.1), the recommender system may be configured to provide a controlstructure where the addition of a new content source triggers an updateof the pre-selection filter so that content items from the new contentsource can pass the pre-selection filter. As an example, it is assumedthat a predetermined personal “western” channel is defined to filter outall movies but western movies, broadcast on a number of predefinedchannels. If a user now subscribes to a new video-on-demand service, thepre-selection filter of the “western” channel can be updated or extendedto include the new video-on-demand service as a new content source.

Additionally, in systems where multiple pre-selection filters (eachconnected to its own recommender engine) are used to define “virtual” or“personal” channels, a subscription to a new service may trigger adialogue with the user to select one or more appropriate virtualchannels or to create a new virtual channel (thus instantiating a newpre-selection filter) to which the new service or content source shouldcontribute.

For each selected virtual channel, filter definitions or parameters canbe updated to allow inclusion of appropriate or desired content itemsfrom the new content source. In this, case, the above mentioned biasingfunctionality of the biased recommender engine can be applied to eachselected virtual channel.

It is noted that the present invention can be applied to any recommendersystem for set-top boxes, TV sets, mobile phones, personal digitalassistants (PDAs), personal computers (PCs) and all devices whererecommenders are used to collect, filter, and present content items frommultiple sources to their users. The invention is thus not restricted torecommenders of television or film content, but can be applied to music,theatre shows, books and all types of products and services for whichrecommenders can be built.

In summary, the present invention relates to an apparatus, a method anda computer program product for controlling a recommender system, whereina bias is applied to the output of a recommender in order to favournewly added services and content sources. The bias may decay in timeand/or with the number of ratings given to content coming from the newservice.

While the invention has been illustrated and described in detail in thedrawings and the foregoing description, such illustration anddescription are to be considered illustrative or exemplary and notrestrictive. The invention is not limited to the disclosed embodiments.From reading the present disclosure, other modifications will beapparent to persons skilled in the art. Such modifications may involveother features which are already known in the art and which may be usedinstead of or in addition to features already described herein.

Variations to the disclosed embodiments can be understood and effectedby those skilled in the art, from a study of the drawings, thedisclosure and the appended claims. In the claims, the word “comprising”does not exclude other elements or steps, and the indefinite article “a”or “an” does not exclude a plurality of elements or steps. A singleprocessor or other unit may fulfill at least the functions of FIG. 2based on corresponding software routines. The computer program may bestored/distributed on a suitable medium, such as an optical storagemedium or a solid-state medium supplied together with or as part ofother hardware, but may also be distributed in other forms, such as viathe Internet or other wired or wireless telecommunication systems. Themere fact that certain measures are recited in mutually differentdependent claims does not indicate that a combination of these measurescannot be used to advantage. Any reference signs in the claims shouldnot be construed as limiting the scope thereof.

What is claimed is:
 1. A biased recommender apparatus for controlling arecommender system, said apparatus comprising a signal processingdevice, a computer device, or a chip, and at least one memory includinga software routine or program, the at least one memory and the softwareroutine or program configured to with the signal processing device,computer device, or chip, cause the apparatus to: a) calculate, based oninformation data of a content item together with data indicating like ordislike of a user, a predicted rating value for a content item receivedfrom a content source; b) determine an age of subscription to saidcontent source so as to derive an age parameter of said content source;and c) apply a correction value to said predicted rating value based onsaid age parameter in order to favour content items of new contentsources.
 2. The apparatus according to claim 1, wherein said determiningan age of subscription determines a subscription time based on metadata,and calculates said age parameter based on said subscription time and anactual time.
 3. The apparatus according to claim 2, wherein saiddetermining an age of subscription determines said subscription time byaccessing a look-up table.
 4. The apparatus according to claim 1,wherein said applying a correlation value to said predicted rating valuedecreases said correction value with increasing age parameter or withincreasing number of rating values for received content items of saidcontent source.
 5. The apparatus according to claim 4, wherein saidapplying a correlation value to said predicted rating value decreasessaid correction value in a linear or exponential relation to time orsaid number of rating values.
 6. The apparatus according to claim 1,wherein said calculating a predicted rating value calculates saidpredicted rating value in a range [0, 1].
 7. The apparatus according toclaim 1, wherein said age parameter is a novelty value which indicateshow long said content source has been available in said recommendersystem.
 8. The apparatus according to claim 7, wherein said noveltyvalue is a positive real number less or equal to
 1. 9. The apparatusaccording to claim 1, wherein said correction value results in a higherrating value for younger subscriptions.
 10. A method of controlling arecommender system, said method comprising: a) calculating, based oninformation data of a content item together with data indicating like ordislike of a user, a predicted rating value for a content item receivedfrom a content source; b) determining an ace of subscription to saidcontent item so as to derive an age parameter of said content source;and c) applying a correction value to said predicted rating value basedon said age parameter in order to favour content items of new contentsources.
 11. The method according to claim 10, further comprisingtriggering an update of a content pre-selection filter in response to anaddition of a new content source.
 12. The method according to claim 10,further comprising triggering in response to an addition of a newcontent source, a user input operation for creating a new channel withpre-selection filter to the new content source.
 13. A computer programproduct comprising producing signal processing device, or computerdevice, or chip, and at least one memory including a software routine orprogram, the at least one memory and the software routine or programconfigured to with the signal processing device or computer device,cause the computer program product at least to perform the steps ofmethod claim 10.